Spatial and temporal variation of surface ozone, NO and NO₂ at urban, suburban, rural and industrial sites in the southwest of the Iberian Peninsula

Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach

Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors.

Spatiotemporal variation of ozone pollution and health effects in China

With the rapid urbanization and industrialization in China, ozone pollution has become increasingly serious and poses a greater threat to human health. In this study, the spatiotemporal distribution of ozone pollution in China's cities and urban agglomerations from 2015 to 2019 was analyzed. The health effects and health economic costs of ozone pollution in China were estimated by applying the environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results are as follows: (1) ozone pollution was more serious in Chinese urban agglomerations from 2015 to 2019; (2) the hot spots of ozone concentration mainly distributed in the North China Plain, expanding from north to south; the cold spots decreased year by year and were located in the northeast, northwest, and southwest of China, shifting from northwest to southwest; (3) the seasonal average of ozone concentration in China was the highest in summer, followed by spring and autumn, and the lowest in winter; (4) the number of all-cause premature deaths of ozone pollution in China increased slowly from 2015 to 2019, and the average of urban agglomerations was significantly higher than cities, with similar spatial distribution characteristics as ozone concentration; (5) the health economic costs of ozone pollution from 2015 to 2019 slowly expanded to surrounding cities with Beijing, Shanghai, Xi'an, and Chongqing as the centers of high values, while the low value areas decreased year by year and were mainly concentrated in southwest and northeast China. The health economic costs of ozone pollution at urban agglomerations scale were higher in the eastern coastal regions and lower in the northwest inland regions. Thus, this study presents policy recommendations to provide decision-making reference for realizing the inter-regional prevention and control of ozone pollution.

Temporal variations, regional contribution, and cluster analyses of ozone and NOx in a middle eastern megacity during summertime over 2017-2019

Particulate matter is usually regarded as the dominant pollutant in Tehran megacity in Iran. However, the number of ozone exceedance days significantly increased in recent years. This study analyzes simultaneous measurements of O₃ and NO_x (NO+NO₂) concentrations to improve our understanding of ozone evolution during the summers of 2017 to 2019. The k-means clustering technique was used to select five representative air quality monitoring sites in Tehran to capture O₃ and NO_x concentrations' variability. The findings show that all of the investigated sites failed to meet the ozone non-attainment criterion. The ozone weekend effect is seen in the study of weekday/weekend differences in 2017 and 2018, but not in 2019, which can be due to the shift in the ozone production regime. The summer mean variation analysis can also be used to deduce this regime change. In 2017, the O₃ and NO₂ summer mean variations suggest a holdback in the NO₂ upward trend and a reversal in the O₃ downward trend that had been in place since 2012. Air mass back trajectory clustering reveals that east and north-east air mass clusters have the most significant impact on Tehran's O₃ pollution and the highest regional contribution to O_X. The study of O_X against NO_x shows that the regional contribution to O_X increased from 2017 to 2018 and then decreased in 2019; however, the local contribution is the opposite. The diurnal analysis of the regional and local contributions to O_X indicated that O_X in Tehran might be primarily affected by pollutants from a short distance. The findings reveal critical changes in the behavior of O₃ in recent years, indicating that decision-makers in Tehran should reconsider air pollution control measures.

Surface Ozone Pollution: Trends, Meteorological Influences, and Chemical Precursors in Portugal

Surface ozone (O3) is a secondary air pollutant, harmful to human health and vegetation. To provide a long-term study of O3 concentrations in Portugal (study period: 2009–2019), a statistical analysis of ozone trends in rural stations (where the highest concentrations can be found) was first performed. Additionally, the effect of nitrogen oxides (NOx) and meteorological variables on O3 concentrations were evaluated in different environments in northern Portugal. A decreasing trend of O3 concentrations was observed in almost all monitoring stations. However, several exceedances to the standard values legislated for human health and vegetation protection were recorded. Daily and seasonal O3 profiles showed high concentrations in the afternoon and summer (for all inland rural stations) or spring (for Portuguese islands). The high number of groups obtained from the cluster analysis showed the difference of ozone behaviour amongst the existent rural stations, highlighting the effectiveness of the current geographical distribution of monitoring stations. Stronger correlations between O3, NO, and NO2 were detected at the urban site, indicating that the O3 concentration was more NOx-sensitive in urban environments. Solar radiation showed a higher correlation with O3 concentration regarding the meteorological influence. The wind and pollutants transport must also be considered in air quality studies. The presented results enable the definition of air quality policies to prevent and/or mitigate unfavourable outcomes from O3 pollution.

Understanding ozone pollution in the Yangtze River Delta of eastern China from the perspective of diurnal cycles

Ozone (O3) pollution has aroused increasing attention in China in past years, especially in the Yangtze River Delta (YRD), eastern China. Ozone and its precursors generally feature different diurnal patterns, which is closely related to atmospheric physical and chemical processes. This work aims to shed more light on the causes of ozone pollution from the perspective of the diurnal patterns. Hundreds of ozone pollution days (with maximum hourly O3 concentration over 100 ppb) during 2013-2017 were identified and then clustered into 4 typical types according to the diurnal variation patterns. We found that ozone pollution in Shanghai was particularly severe when anthropogenic pollutant mixed with biogenic volatile organic compounds (BVOCs) under the prevailing southwesterly wind in summer. The reason could be attributed to the spatial disparities of ozone sensitivity regime in YRD: VOC-limited regime around in the urban area and NOx-limited regime in the rural forest regions in the southern and southwest. The transition of sensitivity regimes along south/southwest wind tended to promote the photochemical production of ozone, making daily O3 pollution time exceeding 6 h of the day. In addition, ozone peak concentration in Shanghai was highly dependent on the evolution of sea-land breezes (SLBs). Earlier sea breeze associated with approaching typhoon in the West Pacific caused less cloud (-25%) and more solar radiation (11%) in YRD, which subsequently led to a rapid increase of O3 concentration in the morning and a deteriorated ozone pollution during noon and the afternoon. This study highlights the importance of observation-based processes understanding in air quality studies.

Improving graph-based detection of singular events for photochemical smog agents

Recently, a set of graph-based tools have been introduced for the identification of singular events of O₃, NO₂ and temperature time series, as well as description of their dynamics. These are based on the use of the Visibility Graphs (VG). In this work, an improvement of the original approach is proposed, being called Upside-Down Visibility Graph (UDVG). It adds the possibility of investigating the singular lowest episodes, instead of the highest. Results confirm the applicability of the new method for describing the multifractal nature of the underlying O₃, NO₂, and temperature. Asymmetries in the NO₂ degree distribution are observed, possibly due to the interaction with different chemicals. Furthermore, a comparison of VG and UDVG has been performed and the outcomes show that they describe opposite subsets of the time series (low and high values) as expected. The combination of the results from the two networks is proposed and evaluated, with the aim of obtaining all the information at once. It turns out to be a more complete tool for singularity detection in photochemical time series, which could be a valuable asset for future research.

Checking complex networks indicators in search of singular episodes of the photochemical smog

A set of indicators derived from the analysis of complex networks have been introduced to identify singularities on a time series. To that end, the Visibility Graphs (VG) from three different signals related to photochemical smog (O₃, NO₂ concentration and temperature) have been computed. From the resulting complex network, the centrality parameters have been obtained and compared among them. Besides, they have been contrasted to two others that arise from a multifractal point of view, that have been widely used for singularity detection in many fields: the Hölder and singularity exponents (specially the first one of them). The outcomes show that the complex network indicators give equivalent results to those already tested, even exhibiting some advantages such as the unambiguity and the more selective results. This suggest a favorable position as supplementary sources of information when detecting singularities in several environmental variables, such as pollutant concentration or temperature.

Can complex networks describe the urban and rural tropospheric O3 dynamics?

Tropospheric ozone (O₃) time series have been converted into complex networks through the recent so-called Visibility Graph (VG), using the data from air quality stations located in the western part of Andalusia (Spain). The aim is to apply this novel method to differentiate the behavior between rural and urban regions when it comes to the ozone dynamics. To do so, some centrality parameters of the resulting complex networks have been investigated: the degree, betweenness and shortest path. Some of them are expected to corroborate previous works in order to support the use of this technique; while others to supply new information. Results coincide when describing the difference that tropospheric ozone exhibits seasonally and geographically. It is seen that ozone behavior is fractal, in accordance to previous works. Also, it has been demonstrated that this methodology is able to characterize the divergence encountered between measurements in urban environments and countryside. In addition to that, the promising outcomes of this technique support the use of complex networks for the study of air pollutants dynamics. Particularly, new nuances are offered such as the identification and description of singularities in the signal.

City-Specific Air Quality Warnings for Improved Asthma Self-Management

INTRODUCTION

This study presents a framework for identifying "high-risk" days for asthma attacks associated with elevated concentrations of criteria pollutants using local information to warn citizens on days when the concentrations differ from Environmental Protection Agency Air Quality Index (AQI) warnings. Studies that consider the unique mixture of pollutants and the health data specific to a city provide additional information for asthma self-management. This framework is applied to air pollution and asthma data to identify supplemental warning days in Houston, Texas.

METHODS

A four-step framework was established to identify days with pollutant levels that pose meaningful increased risk for asthma attacks compared with baseline. Historical associations between 18,542 ambulance-treated asthma attacks and air pollutant concentrations in Houston, Texas (2004-2016; analyzed in 2018), were analyzed using a case-crossover study design with conditional logistic regression. Days with historically high associations between pollution and asthma attacks were identified as supplemental warning days.

RESULTS

Days with 8-hour maximum ozone >66.6 parts per billion for the 3 previous days and same-day 24-hour nitrogen dioxide >19.3 parts per billion pose an RR of 15% above baseline; concentrations above these levels pose an increased risk of 15% (RR=1.15, 95% CI=1.14, 1.16) and 30% (RR=1.30, 95% CI=1.29, 1.32), respectively. These warnings add an additional 12% days per year over the AQI warnings.

CONCLUSIONS

Houston uses this framework to identify supplemental air quality warnings to improve asthma self-management. Supplemental days reflect risk lower than the National Ambient Air Quality Standards and consecutive poor air quality days, differing from the AQI.

Visibility graphs of ground-level ozone time series: A multifractal analysis

A recent method based on the concurrence of complex networks and multifractal analyses is applied for the first time to explore ground-level ozone behavior. Ozone time series are converted into complex networks for their posterior analysis. The searched purpose is to check the suitability of this transformation and to see whether some features of these complex networks could constitute a preliminary analysis before the more thorough multifractal formalism. Results show effectively that the exposed transformation stores the original information about the ozone dynamics and gives meaningful knowledge about the time series. Based on these results, the multifractal analysis of the complex networks is performed. Looking at the physical meaning of the multifractal properties (such as fractal dimensions and singularity spectrum), a relationship between those and the degree distribution of the complex networks is found. In addition to all the promising results, this novel connection between time series and complex networks can deal with both stationary and non-stationary time series, overcoming one of the main limitations of multifractal analysis. Therefore, this technique can be regarded as an alternative to give supplementary information within the study of complex signals.

A Predictive Model for Steady State Ozone Concentration at an Urban-Coastal Site

Ground level ozone (O₃) plays an important role in controlling the oxidation budget in the boundary layer and thus affects the environment and causes severe health disorders. Ozone gas, being one of the well-known greenhouse gases, although present in small quantities, contributes to global warming. In this study, we present a predictive model for the steady-state ozone concentrations during daytime (13:00⁻17:00) and nighttime (01:00⁻05:00) at an urban coastal site. The model is based on a modified approach of the null cycle of O₃ and NO_x and was evaluated against a one-year data-base of O₃ and nitrogen oxides (NO and NO₂) measured at an urban coastal site in Jeddah, on the west coast of Saudi Arabia. The model for daytime concentrations was found to be linearly dependent on the concentration ratio of NO₂ to NO whereas that for the nighttime period was suggested to be inversely proportional to NO₂ concentrations. Knowing that reactions involved in tropospheric O₃ formation are very complex, this proposed model provides reasonable predictions for the daytime and nighttime concentrations. Since the current description of the model is solely based on the null cycle of O₃ and NO_x, other precursors could be considered in future development of this model. This study will serve as basis for future studies that might introduce informing strategies to control ground level O₃ concentrations, as well as its precursors' emissions.

Associations between air pollution in the industrial and suburban parts of Ostrava city and their use

Selecting the locations and numbers of air quality monitoring stations is challenging as these are expensive to operate. Representative concentrations of pollutants in certain areas are usually determined by measuring. If there are significant correlations with concentrations of other pollutants or with other monitoring sites, however, concentrations could also be computed, partly reducing the costs. The aim of this study is to provide an overview of such possible relationships using data on concentrations of ambient air pollutants obtained in different areas of a larger city. Presented are associations between industrial (IP) and suburban parts (SP) as well as correlations between concentrations of various pollutants at the same site. Results of air pollutant monitoring come from Ostrava, an industrial city in Central Europe with a population of over 300,000. The study showed that certain pollutants were strongly correlated, especially particulate matter (r = 0.940) and ozone (r = 0.923) between the IP and SP. Statistically significant correlations were also found between different pollutants at the same site. The highest correlations were between PM₁₀ and NO₂ (r _IP = 0.728; r _SP = 0.734), NO₂ and benzo(a)pyrene (r _IP = 0.787; r _SP = 0.697), and NO₂ and ozone (r _IP = -0.706; r _SP = -0.686). This could contribute to more cost-effective solutions for air pollution monitoring in cities and their surroundings by using computational models based on the correlations, optimization of the network of monitoring stations, and the best selection of measuring devices.

Atmospheric pollutants in peri-urban forests of Quercus ilex: evidence of pollution abatement and threats for vegetation

Peri-urban vegetation is generally accepted as a significant remover of atmospheric pollutants, but it could also be threatened by these compounds, with origin in both urban and non-urban areas. To characterize the seasonal and geographical variation of pollutant concentrations and to improve the empirical understanding of the influence of Mediterranean broadleaf evergreen forests on air quality, four forests of Quercus ilex (three peri-urban and one remote) were monitored in different areas in Spain. Concentrations of nitrogen dioxide (NO2), ammonia (NH3), nitric acid (HNO3) and ozone (O3) were measured during 2 years in open areas and inside the forests and aerosols (PM10) were monitored in open areas during 1 year. Ozone was the only air pollutant expected to have direct phytotoxic effects on vegetation according to current thresholds for the protection of vegetation. The concentrations of N compounds were not high enough to directly affect vegetation but could be contributing through atmospheric N deposition to the eutrophization of these ecosystems. Peri-urban forests of Q. ilex showed a significant below-canopy reduction of gaseous concentrations (particularly NH3, with a mean reduction of 29-38%), which indicated the feasibility of these forests to provide an ecosystem service of air quality improvement. Well-designed monitoring programs are needed to further investigate air quality improvement by peri-urban ecosystems while assessing the threat that air pollution can pose to vegetation.